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Distribution-based entropy weighting clustering of skewed and heavy tailed time series
dc.contributor.author | Mattera, Raffaele |
dc.contributor.author | Giacalone, Massimiliano |
dc.contributor.author | Gibert, Karina |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa |
dc.date.accessioned | 2022-01-18T10:18:53Z |
dc.date.available | 2022-01-18T10:18:53Z |
dc.date.issued | 2021-05-28 |
dc.identifier.citation | Mattera, R.; Giacalone, M.; Gibert, K. Distribution-based entropy weighting clustering of skewed and heavy tailed time series. "Symmetry-Basel", 28 Maig 2021, vol. 13, núm. 6, article 959. |
dc.identifier.issn | 2073-8994 |
dc.identifier.uri | http://hdl.handle.net/2117/359885 |
dc.description.abstract | The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks. |
dc.language.iso | eng |
dc.rights | Attribution 4.0 International |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
dc.subject.lcsh | Operations research |
dc.subject.lcsh | Stochastic analysis |
dc.subject.other | Classification |
dc.subject.other | Generalized error distribution |
dc.subject.other | Skewness |
dc.subject.other | Skewed exponential power distribution |
dc.subject.other | Financial time series |
dc.subject.other | Portfolio selection |
dc.title | Distribution-based entropy weighting clustering of skewed and heavy tailed time series |
dc.type | Article |
dc.subject.lemac | Investigació operativa |
dc.subject.lemac | Anàlisi estocàstica |
dc.contributor.group | Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
dc.identifier.doi | 10.3390/sym13060959 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.ams | Classificació AMS::90 Operations research, mathematical programming::90B Operations research and management science |
dc.subject.ams | Classificació AMS::60 Probability theory and stochastic processes::60G Stochastic processes |
dc.relation.publisherversion | https://www.mdpi.com/2073-8994/13/6/959 |
dc.rights.access | Open Access |
local.identifier.drac | 32013335 |
dc.description.version | Postprint (published version) |
local.citation.author | Mattera, R.; Giacalone, M.; Gibert, Karina |
local.citation.publicationName | Symmetry-Basel |
local.citation.volume | 13 |
local.citation.number | 6, article 959 |
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